Modeling and Simulation of Wax Deposition in Crude Oil … and Simulation of Wax Deposition in Crude Oil Pipeline Dr Ratnadip R Joshi Associate Dean and Professor, Department of Petroleum

Modeling and Simulation of Wax Deposition in Crude Oil Pipeline

Dr Ratnadip R Joshi

Associate Dean and Professor, Department of Petroleum and Petrochemical Engineering,

Maharashtra Institute of Technology,

124, Paud Road, Kothrud, Pune, India

Abstract

As a fluid flows through a subsea pipeline, a cooling process

occurs due to heat loss to the surrounding seawater. Wax may

precipitate as a solid phase when the bulk temperature drops

below the Wax Appearance Temperature (WAT), and wax can deposit on the pipe wall. Untreated wax deposition leads

to reduced flow area, and to prevent blockage of the pipe a

wax deposition model is used to predict the amount of wax to

expect. Turbulent flow, single-phase experiments were

performed with a waxy gas-condensate. The Hydro model,

which takes into account asymptotic growth, seemed invalid

for these experiments. The RRR and the Matzain model are

available in the transient thermo hydraulic simulation

software. Simulation studies have been carried out in order to

find out the pressure and temperature drops in the pipeline

under various operating scenarios and different ambient temperatures. Scenarios such as steady state flow (CTF Line /

SRJ GGS), wax deposition and pigging, Skin Heat Tracing

system (SHTS), alternate pumping of oil and water, higher

dispatch temperature and additional line from field RRJ to

field SRJ are developed. Results of wax deposition and

pigging are discussed in this paper.

Keywords: crude oil pipeline, wax deposition, wax deposition

model, transient thermo hydraulic simulation, Wax Deposition

and Pigging

Introduction There are several wax deposition models with different

approaches for modelling wax deposition.[1,2,3] In reality, the

physical processes that constitute the wax deposition process

are not properly discussed and the process of deposition is still

poorly understood. Velocity determination applications such

as gasification systems elaborate on momentum aspects in tubular lines. [4,5,6]

Most of the focus concerning formation and deposition of wax

has been put on single-phase transportation of paraffinic oils,

but significant problems with wax deposition may also occur

in unstabilized oil and condensate systems.[7] An accurate

prediction of wax deposition rates and deposited wax

distribution in pipelines would represent high value for the

industry. This information is valuable for the design stages of

the field and also in the scheduling of intervention in the

pipeline, in order to assure the oil flow at the desired rate.

In this paper, two wax deposition models are described. These

are the RRR (Rygg, Rydahl and Rønningsen) model and the

Matzain model. Simulations are carried out by using the RRR

and the Matzain model, which are validated using transient

simulator. The most important element is to illustrate how wax deposition models predict wax build-up.

The RRR model

The RRR (Rygg, Rydahl and Rønningsen) model is a multi-

phase flow wax deposition model which predicts wax

deposition in wells and pipelines. Note that the RRR model is

not applicable for laminar flow.[8] A standard steady state

multi-phase point model is used to predict pressure drop and

liquid hold-up along the pipeline. The effect of deposition on

pressure drop and temperature is calculated by integration in

time. The multi-component wax model continuously estimates the wax precipitation along the pipeline and the viscosity of

the composition. The wax deposition is then estimated from

the diffusion of wax from the bulk towards the surface of the

pipeline, due to temperature gradients and shear dispersion

effect. The inner pipe wall friction is varied due to wax

deposition.[1,7,9,10]

The wax deposition model is divided into separate sub-

models, where each sub-model describes specific technical

aspect and the components are treated individually.[7,9,13]

Sub-models: Flow model calculates pressure drop and flow regime

Thermodynamic Wax Model (TWM) determines the

number and properties of the different phases for

each section

Viscosity model calculates the viscosity

Wax deposition model predicts the amount of wax

that deposits in a section of the pipeline.

Wax deposition build-up is a slower process than flow

disturbances in a pipeline. Therefore a semi-stationary model

is chosen for wax deposition predictions over longer periods. Semi-stationary is described as changes in boundary

conditions like flow rates, pressures and temperature are taken

into account along with varying inner pipe diameter. The

model is also compositional, meaning that all components are

treated individually in each sub-model. A component mass

balance is used for track keeping of all components.[7,10]

Hydro model

The Hydro in-house wax deposition model was developed at

Norsk Hydro by Søntvedt. [2,6] Hydro used the model a few

times, but the model has never been reported or documented

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officially. Previously this deposition model was implemented

in an Excel spreadsheet, solved analytically and based on

single-phase homogeneous flow. Today programming

languages and simulators solve the governing equations

numerically.[9, 14] The model is implemented in an in-house

multiphase flow and heat transfer model, WellCorr.[11] The model estimates the amount of deposited wax over a time

period inside a pipe, and it takes into account that deposition

during time shows an asymptotic growth due to the stress

from fluid flow. The model is based on theory of asymptotic

fouling, which is known from the heat exchange theory. This

theory is able to explain the asymptotic level of deposits seen

in experiments.[11] Previous experiments performed at the

wax rig in Porsgrunn used three different oils. This makes the

model more robust when extrapolating to other oils.[11,13]

The auto retardation mechanism is described mechanistically

which is not based on tuning of a shear coefficient for shear

removal. The deposit porosity is described mechanistically. This factor is a tuning parameter in other models. The Hydro

model does not include shear dispersion. The Hayduk and

Minhas correlation (1982) [12,13] is used for calculating the

diffusion coefficient. The theory of fouling of heat exchanger

equipment was suggested applicable for the mechanism of

wax deposition in pipelines. Accumulation of deposit on a

cold surface occurs due to mechanisms depending upon the

fluid and the flow conditions.

Methodology

The pipeline is discretized into a number of sections, where pressure and composition are assumed constant within one

section. The temperature gradient reaches from the centre of

the pipe to the pipe wall. The energy balance determines the

bulk and wall temperatures. All sections are simulated, and

independent pressure drops for each section are added up and

gives the total pressure drop.[7,9]

Wax deposited in one period affects the results in the

following time periods. Wax deposits in one section leads to

decreased diameter, thus higher pressure drop in the pipeline.

Additionally the energy balance is influenced, because the

wax has an insulating effect in the pipeline. This results in increased temperature over time for a waxy section.[7,9]

Deposition in the RRR model is based on molecular diffusion

and shear dispersion, both mechanisms which enhance the

wax deposition. [7] The volume rate of wax deposition by

molecular diffusion for a wax forming composition i is found

from

Eq. 1

Where cib & ci

w are the molar concentrations of the wax component i dissolved in the oil phase in the bulk and at the

wall respectively (mole/m3), wet S is the fraction of the wetted

circumference, NWAX is the number of wax components,

MWi is the molar weight of wax component i (kg/mole), ρi is

the density of wax component i (kg/m3), r is the current inner

pipe radius (m) and L is the length of the pipe section (m). D

is the diffusion coefficient, and the Hayduk-Minhas

correlation (m2/s)[8] is used to calculate the diffusion

coefficient. δ is the thickness of the laminar sub-layer (m), and

Blasius equation is used when calculating the thickness of the

sub-layer in turbulent flow.[7]

Also the volume rate of wax deposited from shear dispersion can be estimated from a correlation by Burger et al.

(1981)[15]

Eq. 2

When accounting for both mechanisms, the total rate of

increase in thickness for the wax layer is given as

Eq. 3

Where φ is the porosity of the deposited wax. The porosity is

usually assumed to be in the range from 0.6-0.9. Wax layer

porosity is an adjustable parameter in the model. Note that the

thickness of the wax layer is averaged around the pipe

circumference, even if the inner pipe surface is only partially

wetted with liquid. For multi-phase flow the wetted inner

surface area depends on the local flow regime predicted and the liquid hold-up. [7]

The RRR model has been applied to several single and multi-

phase pipeline systems. In two cases presented by Rygg et al.

(1998) [7], wax-build-up, temperature and pressure drops

were simulated over time. Good agreement was shown

between calculated and observed pressure losses. Important

tuning parameters in this model are the wax layer porosity

and the roughness of the wax deposit. [7,16,17,18]

An assumption made for this model is that the precipitation

rate at the wall is not a limiting factor, and that all wax transported to the wall will stick to the surface as long as the

temperature is below WAT.[19,20] It should be noticed that

no removal mechanisms are discussed in this model.

Therefore, the main drawback concerning the RRR model is

the lack of shear incorporated into the model. However, the

model may give good enough predictions for low flow

rates.[21,22]

Additionally, this model may give reasonable predictions at

the time when wax starts to grow at a clean pipe, but after

some time the results become less reliable, due to no release-mechanism included in the model.

Modeling the Process

Line pressure trend (RRJ-SRJ line)

Presently the production from RRJ field is about 500m3/d.

The crude from RRJ is pumped to GGS-1. At RRJ GGS-1 the

entire RRJ crude is heated to 55oC and pumped in RRJ-SRJ-

CTF crude dispatch line. Due to viscous nature of RRJ crude

high pressure drop is observed during winters in the RRJ-SRJ

sector.

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Fig. 1: Monthly pressure variation

Fig. 2: Modelling of temperature and pressure parameters

Fig. 3: Modelling of volume and pressure parameters

The crude parameters used for developing the simulation

model are as follows:

Table 1: Crude properties used for simulation

No Properties Value

1 Sp. Gravity 60/60oF 0.864-0.868

2 API Gravity 60oF 32

3 Pour Point (oC) 36-42

4

Viscosity Dry crude CP 363

@45oC , 300@ 25oC

5

Viscosity emulsion (50% w/c) CP

233 @45 oC, 225@ 25oC

6

Viscosity emulsion (90% w/c) CP

233 @45 oC, 437@ 25oC

7 STO(Wax) WT % 22-26

Results of Simulation Studies for RRJ – SRJ line The RRJ SRJ sector is being pigged for scrapping of wax once

in a month. Simulation has been carried out to predict the wax

deposition possibility. The fluid modeling has been carried to

capture the fluid parameters. The simulation results

representing winter conditions i.e. ambient 10oC are presented below.

Fig. 4: Wax Mass vs Days

Fig. 5: Wax thickness along the line

Fig. 6: Wax removal by pigging after 30 days deposition

Fig. 6: Pig velocity and travel time

The pigging case simulation results for winter conditions i.e

ambient 10 deg C reveals the following results:

Table 2: The pigging case simulation results

No. Particulars Results

1 Wax deposition Mass in 30 days ~100 kg

2

Distance propensity from

RRJ GGS-I ~ 5 kms

3 Peak thickness 0.3 mm

4 Average Pig Velocity 0.18 m/s

5

Pig travel time

(RRJ GGS1-SRJ-GGS) ~41 hrs

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Conclusions Wax deposition in pipelines represents a great challenge for

the petroleum industry. By using wax deposition models, it is

possible to predict the wax thickness and the pigging

frequency can be estimated.

From the present study it is concluded that: • High back pressure during winters in RRJ-SRJ and crude

dispatch line is due to highly viscous crude

• Wax deposition (30 days) does not impact back pressure

• Pigging frequency of 30 days is found optimum

• Higher dispatch temperature, alternate oil-water pumping,

diversion to intermediate tanks resulted in reduced back

pressure

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